An Optimal Transfer Learning Approach for Diabetic Retinopathy Severity Level Detection with Cluster Based Image Segmentation
Keywords:
Diabetic Retinopathy, Deep Learning, Pre-Trained Model, Dataset balancing, Segmentation, Feature Extraction, and Retinal Fundus ImagesAbstract
Diabetic Retinopathy is a mutual snag resulting from diabetes, which affects the retina. It is the leading cause of blindness worldwide and detecting this disease early can prevent it from causing patients to lose their sight. But the early detection of Diabetic Retinopathy is very tough and requires an expert interpretation on fundus images by clinical specialists. Conclusions: A novel deep learning-based diabetic retinopathy classification with integrated data balancing transfer learning for the feature selection approach was recommended in this study. The system includes 4 phases including; dataset balancing, preprocessing, segmentation and classification. The system initially carries out data balancing by the Near-miss algorithm. The preprocessing is followed up where the images are enhanced by Retinex algorithm and image augmentation is performed for enhancing the dataset quality. After which, the Deviation cantered K-Means (DKM) algorithm is used to segment. Useful features are extracted and classified from the segmented images using Optimal Vector Pooling Centred Exception Network (OVPXNet). We tested and validated the system to attain higher accuracy of 99.66% for MESSIDOR-2 dataset, average accuracy achieved: i.e., APTOS &MESSIDOR-2 was 99.59%.
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